计算机工程与应用2024，Vol.60Issue(8)：90-98,9.DOI:10.3778/j.issn.1002-8331.2211-0261
基元库构建思想的机器人动作与策略演示学习方法
Robotic Actions and Strategy Demonstration Learning Method for Constructing Primitive Library Ideas
摘要
Abstract
In order to solve the problems of demonstration data optimization,action and task strategy storage and call in the process of robot demonstration learning,a demonstration learning method based on primitive library is proposed.Action learning uses experts to drag the manipulator to perform actions to obtain demonstration data.Gaussian mixture model and Gaussian mixture regression are used to improve the data quality,and the final demonstration data is converted into the weight value of the basis function by the dynamic motion primitive algorithm.Strategy learning creates task steps as action primitives,adds the obtained weight value to the primitives,builds the primitive business card containing task execution strategy,and forms the primitive library to complete storage.When executing tasks,the primitives are sequen-tially called from the primitive library.YOLOv5 target detection network and AlexNet image classification network are used to detect target information to match actions and generalize new actions with original action characteristics.This method realizes learning actions and strategy storage from the demonstration,and combining appropriate actions to complete tasks according to actual goals.According to the experiment of steel bar binding scene,5 action primitives are created,10 basic actions are learned through expert teaching,the robot successfully completes the lashing task at the inter-section of horizontal and vertical reinforcement by using the action primitive library.关键词
演示学习/轨迹模仿学习/任务策略学习/动态运动基元/运动基元库Key words
demonstration learning/trajectory imitation learning/task strategy learning/dynamic motion primitives/motion primitive library分类
信息技术与安全科学引用本文复制引用
李铁军,刘家奇,刘今越,贾晓辉..基元库构建思想的机器人动作与策略演示学习方法[J].计算机工程与应用,2024,60(8):90-98,9.基金项目
国家自然科学基金(U20A20283,U181320104). （U20A20283,U181320104）
